Optimization of downsampling occurring before encoding images during compression

ABSTRACT

The disclosure lies in the field of coding image data, scalable in image resolution, such that one or more successive downsamplings are performed from a source signal of maximal image resolution to a minimal image resolution, in order to encode a base layer (L0) corresponding to this minimal resolution, the base layer (L0) serving as a reference for one or more enhancement layers (L1, L2) to be used for decoding at a terminal having a screen of higher image resolution than the minimal image resolution. From said successive downsamplings, the base layer (L0) and the one or more enhancement layers (L1, L2) are constructed, and then an encoded bit stream is prepared in order to be transmitted, comprising data of the base layer and of the enhancement layer or layers. In particular, the downsampling step comprises the application of an adaptive low-pass filter to the image data from the downsampling.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority benefit under 35 U.S.C. § 119(d) fromFrench Patent Application No. 18 59493 filed Oct. 12, 2018, the entirecontent of which is incorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates to the field of video encoding/decoding,particularly during compression. More particularly, the disclosure aimsto improve the efficiency of coding based on a scalable extension of agiven coding technique.

BACKGROUND

In the HEVC (High Efficiency Video Coding) video coding standard, theextension (SHVC) of this coding technique makes it possible to receiveand decode the image data on different terminals having respectivescreen resolutions, a terminal having a low resolution screen requiringthe use of less data for decoding. Thus, a minimal resolution terminalrequires only a minimal set of data, this minimal set being called a“base layer”, while a higher resolution terminal requires said baselayer and one or more other additional layers. The term “scalable” thusmeans that this type of encoding/decoding supports these differentlayers (in addition to encoding/decoding during compression).

Such a “scalable” encoding/decoding technique makes it possible torespond to the new trends in video consumption and the resultingconstraints. For consumers, these new trends are reflected in theability to watch video content on terminals that may have very differentpicture quality (television, laptop, smartphone, tablet). These newconstraints that compression and video standards must address relate tocoding efficiency, processing speed, and the ability to stream videocontent over one or more channels. In addition, new image formats aredefined in addition to the existing constraints, and consist of anincrease in one or more component characteristics of the video, forexample the image resolution for UHD (Ultra High Definition) format, thecapture and display of images of high dynamic luminance for HRD (HighDynamic Range) format, or the frame rate for HFR (High Frame Rate)format.

These new formats improve the user experience but, in order to satisfythe technical constraints of effective compression and dissemination ofvideo content, scalable video coding remains to be optimized.

More specifically, scalable video coding makes it possible to generate abit stream from a single video source, containing several formats ofthis video source. Depending on the type of scalability, spatial ortemporal for example, the video will be encoded in the bit streamaccording to different resolutions or frame rates.

Each generated layer generally corresponds to a video format, and in thecase of spatial scalability, it refers to a given resolution. Thearchitecture of scalable coding is defined by the aforementioned baselayer, which encodes the lowest resolution. It serves as a reference forthe layers encoding higher resolutions, also called “enhancementlayers”.

These enhancement layers are ranked by ascending order of resolution,starting from the base layer. Each of the enhancement layers may exploitor provide coding information to lower or higher enhancement layers. Alayer is said to be “predicted” if it exploits information from anotherlayer. A layer is said to be a “reference layer” if it providesinformation to another layer. It is thus possible for a predicted layerto exploit the coding of a reference layer. This exploitation is called“inter-layer prediction”. It makes it possible to improve codingefficiency in comparison to an equivalent single-layer coding (sameresolution and same coding parameter).

The prediction efficiency depends both on the prediction toolsthemselves and on the quality of the coding information. This codinginformation passing from one layer to another by conversion mustcorrespond to the destination format. These mechanisms and conversionparameters are defined in the video compression standards.

A method for producing a base layer L0 (intended for a low-resolutionterminal screen, such as a smartphone or other type) and enhancementlayers L1, L2 (for higher-resolution terminal screens, such as acomputer or television screen or other type) is now described in moredetail with reference to FIG. 1. This method is usually implemented at aheadend.

A conversion of the data of the video source is applied at the headendto the lower layers L0, L1 in order to obtain the desired formats forlow resolutions. For spatial scalability, this corresponds to adownsampling DO SAMP.

In the example of scalable video coding (for example according to thescalable extension of the HEVC standard) illustrated in FIG. 1, a bitstream BIN comprising three distinct resolutions (2160p, 1080p, 720p) isobtained as output.

Thus, several interdependent layers are generated from an initial sourcevideo signal (uncompressed in step S10).

Here, three layers L0, L1, and L2 (S13, S15, S17) are generated aspresented below:

-   -   The first two, L0 and L1, are encoded from a succession of        downsampling operations (steps S11, S12) performed on the        initial uncompressed video signal until the base layer L0 is        reached in step S13. These downsampling operations make it        possible to bring the initial video resolution (2160p or more        generally “ultra high definition”) to lower resolutions, here        1080p, then 720p, respectively corresponding to layers L1 and        L0. The lowest resolution layer L0 (constructed at the end of        step S13) is therefore the base layer. It is therefore used as a        reference layer for the higher layers L1 and L2, which are        enhancement layers. Layer L1 (step S15) is both an enhancement        layer, and a reference layer for layer L2;    -   After these downsampling operations, starting from the base        layer L0, inter-layer predictions are estimated, each by means        of an upsampling operation in order to reach a higher resolution        level (step S14) for comparison with the source video signal at        this same higher resolution, in order to construct an        enhancement layer L1 and possibly obtain additional information        on motion vectors at this resolution for inter-image coding for        example;    -   This successive upsampling operations (step S16) are then        continued with comparisons (S17) with the source signal of the        same resolution until the highest resolution layer (L2 in the        illustrated example) is reached; this is done starting from the        lower layers (L0 and L1) to the next higher layer (L2).

These upsampling operations thus contribute to the construction of theenhancement layers. Each enhancement layer corresponds to the additionof coding information supplementing the information given by the lowerlayers.

More precisely, the “comparison” with the source images of the sameresolution makes use of inter-layer prediction operations to exploit anyredundancies in information between the layers. Thus, upsampling isapplied in order to compare information on the same resolution. Forexample, in the case of inter-layer motion prediction, the coordinatesof the motion vector field of the (low resolution) reference layer areresized by upsampling so that they can be used by the predicted layer(of higher resolution).

All the data is then sent to a multiplexer (step S18) which outputs asingle bit stream (BIN), containing the data L0, L1, L2 for threedifferent resolutions in the illustrated example.

In this manner, during video decoding at high resolution (2160p), adecoder reconstructs the video signal at this resolution from the codinginformation of layer L2, but also from coding information present in thelower layers corresponding to lower resolutions (1080p and 720p forexample). In the case of decoding for a 1080p resolution screen, layerL1 is reconstructed based on the coding information of layer L1 as wellas that of layer L0.

Thus, the mechanism of successive downsamplings S11, S12 makes itpossible to progressively reduce the image resolution until the image oflowest resolution is reached, which itself will be compressed byencoding (in step S19 of FIG. 1) to form the base layer L0. One can see,from the construction of the upper layers, that a layer L0 of poorquality has an effect on layers of higher level L1, L2.

There is therefore a need to improve the efficiency of inter-layerprediction, and to ensure that the coding information obtained in layerL0 (or any other reference layer) best benefits the predicted levels ofhigher level.

The present disclosure improves the situation.

DRAWINGS

Other features and advantages of the disclosure will be apparent fromreading the following exemplary embodiments and examining the appendeddrawings in which:

FIG. 1 illustrates a conventional scalable coding,

FIGS. 2A and 2B show aliasing noise filtering combined with adownsampling operation, for illustrative purposes only,

FIG. 3 illustrates an example of selecting a type of filter among a setof filters and possible filter coefficients, according to criteriaidentified in the images to be processed,

FIG. 4 schematically illustrates a device within the meaning of thedisclosure.

DETAILED DESCRIPTION

The present disclosure proposes a method for coding image data, scalablein image resolution, comprising the following steps:

-   -   From a source signal of maximal image resolution, performing one        or more successive downsamplings to a minimal image resolution,        in order to encode a base layer (reference L0 of FIG. 1)        corresponding to said minimal resolution, the base layer (L0)        serving as a reference for one or more enhancement layers (L1,        L2) to be used for decoding at a terminal having a screen of        higher image resolution than the minimal image resolution,    -   From said successive downsamplings, constructing the base layer        (L0) and the one or more enhancement layers (L1, L2),    -   Preparing an encoded bitstream to be transmitted, comprising        data of the base layer and of the one or more enhancement        layers.

In particular, the downsampling step comprises the application of anadaptive low-pass filter to the image data from the downsampling, inorder to reduce a spectral folding effect.

The quality of layer L0, and therefore of all the layers L1, L2 whoseconstruction depends on it, is thus improved over the prior art.

Of course, the construction of the enhancement layer or layers (L1, L2)is carried out, with respect to the base layer (L0), for layers (L1, L2,etc.) which require a downsampling with respect to the source content.If a terminal directly requests the resolution of the source content,the latter is transmitted to it.

Indeed, the downsampling mechanism makes it possible to progressivelyreduce the resolution of a video by deleting a portion of the pixels ofthe images constituting this video. However, such downsampling causes analiasing phenomenon (or “spectral folding”) which is visible in theimages as a degradation of the image quality, particularly in areas ofcontrast. To overcome this aliasing phenomenon, a low-pass filter couldbe used upstream of the downsampling operation.

The complexity of such a low-pass filter must be sufficiently low inorder to make sparing use of the hardware resources needed for thecoding process in general, but must be high enough that the inter-layerprediction remains efficient, at least as efficient as other compressionprocessing methods (for example intra-image and inter-imagepredictions).

Thus, in the context of scalable coding, this downsampling filter mustsatisfy several constraints. Its complexity must be sufficient toeliminate aliasing, while being compatible with the available hardwareresources of the encoder. Next, it must allow optimizing the codingefficiency from a global point of view. The coding efficiency isimproved by the use of inter-layer prediction, which itself depends oncoding information from the reference layers. Finally, the quality ofthe coding information from the reference layers depends on thedownsampling operation performed on the video source. As the coding ofthe reference layers is not simply predictable, currently there is nodownsampling filter model in existence that allows ensuring optimumcoding efficiency for each image or image region.

The disclosure therefore proposes a downsampling filter which inparticular is adaptive, and which makes it possible to reach thisoptimum coding efficiency for each image or image region (according tothe “Bjontegaard” metric as will be seen below).

An adaptive filter may be provided which is directly combined with thedownsampled signal by convolution, for example a finite impulse responsetype of filter in one embodiment.

More particularly, this finite impulse response filter may be of a typechosen among at least:

-   -   a Kaiser-Bessel window filter,    -   a bicubic filter,    -   a bilinear filter,    -   an interpolation filter,        thus presenting a palette of filter types which are more or less        “aggressive” and therefore “smoothing” the image textures to a        greater or lesser extent.

An image pre-analysis before downsampling may then be performed in orderto select a type of adaptive filter according to one or morepredetermined criteria.

In one possible embodiment of such criteria, the type of adaptive filtermay be selected for an image or image region according to at least onecriterion among:

-   -   image resolution before downsampling,    -   frame rate of the source signal,    -   coding rate of the images,    -   importance to be given to one or more regions of interest in        each current image.

Typically, the importance to be given to a region of interest can bequantified by a texture complexity score in that region.

In one possible embodiment, this importance to be given to a region ofinterest in a current image can be measured by:

-   -   a determination of a depth map in the current image, in order to        identify a plurality of superimposed planes,    -   in each of the identified planes, a detection and quantification        of blur,    -   and the assigning of a score to a region according to:        -   a depth of the plane in which this region is located            (typically between a foreground and a background), and        -   a sharpness of the image in this plane.

Thus, for sharp foreground regions, a bicubic filter may be selected,while for sharp background regions and/or fuzzy foreground regions, abilinear filter is used, and otherwise an interpolation filter may beused.

In the context of selecting a Kaiser-Bessel window filter, it ispossible to choose the configuration of such a filter within a range ofconfigurations (defining coefficients of the filter), based on acriterion among image resolution before downsampling and frame rate ofthe source signal.

Finally, the coefficients of the Kaiser-Bessel window filter can bechosen according to a configuration selected within the aforementionedrange, based on the other criterion among image resolution beforedownsampling and frame rate of the source signal.

Additionally or alternatively, a bicubic filter may be selected if thecoding rate of the base layer is greater than a threshold, and otherwisea bilinear filter is selected.

For example, such a threshold may be 2 Mbits/sec.

The present disclosure also relates to a computer program comprisinginstructions for implementing the above method, when these instructionsare executed by a processor of a processing circuit. An algorithm ofsuch a computer program can be illustrated typically by FIGS. 2A, 2B and3, discussed below.

The present disclosure also relates to at an image data coding device,scalable in image resolution, comprising a processing circuit forimplementing the above method. With reference to FIG. 4, such aprocessing circuit CT may typically comprise:

-   -   An input IN to receive the initial image data of the source        signal,    -   A memory MEM for at least temporarily storing said data, and        more particularly instructions of a computer program within the        meaning of the disclosure, as well as filter data (types of        filters and sets of appropriate coefficients, for example),    -   A processor PROC for processing the image data in order to        pre-analyze the images and select at least one type of filter on        the basis of this pre-analysis (or several types for several        regions in a same image), with a set of associated coefficients,        which are appropriate for the image or the image region. For        this purpose, the processor PROC is arranged to cooperate with        the memory MEM, in particular in order to read the instructions        of the computer program and execute them,    -   And an output OUT for delivering image data downsampled and        filtered by the filter thus selected and constructed, these data        then able to be encoded (as illustrated in steps S19, S15 and        S17 presented above with reference to FIG. 1).

The present disclosure lies within a context of an encoding method ofthe type illustrated in FIG. 1. In particular, during the successivedownsampling steps S11, S12, in order to reduce the noise from spectralfolding (or “aliasing”), adaptive filtering is applied (typically alow-pass filter to reduce the aliasing) in respective steps S112 andS122 which follow steps S111 and S121 of downsampling to lowerresolutions, as illustrated in FIGS. 2A and 2B for example. Of course,the three ellipsis dots under the title of FIG. 2B indicate that furtherdownsampling, followed by further adaptive filtering, may be provided(and thus in the other FIGS. 2C, 2D, etc.) for a diversity of more thanthree resolutions.

The parameters, particularly of the adaptive filters (or the types offilters used) may be different from one downsampling to another, asdescribed below.

In one particular embodiment, for each image or image region, the choiceof the type of filter and the coefficients of the filter of the chosentype is made based on criteria related to the content of the source orto the coding itself. For example, these filter selection criteria maybe:

-   -   the initial image resolution (before the downsampling S11 or S12        of FIG. 1, requiring the filtering for which the filter is to be        chosen), or the image “format” below,    -   the number of frames per second in the source signal (or “frame        rate” below),    -   the coding rate of the images to be transmitted (in step S19 of        FIG. 1),    -   the importance to be given to one or more regions of interest in        the image, a texture complexity for example possibly quantifying        this importance (and more particularly the complexity related to        encoding this texture).

The filter type may be a finite impulse response filter, selected from:

-   -   a Kaiser-Bessel window filter,    -   a bilinear filter,    -   a bicubic filter,    -   or an interpolation filter with one or more nearest neighbors.

The indication in this description that “the filter is applied afterdownsampling the signal” is a misuse of language. In reality, a finiteimpulse response filter corresponds to convolution of an input signalsampled by the impulse response of the filter, outputting a downsampledand filtered signal. Thus, in the present description, it is possible totalk about a “downsampling filter”.

The coefficients of the filter can be chosen according to the differentpossible parameters of the signal to be filtered. For example, in thecase of using a Kaiser-Bessel window filter, the choice of coefficientscan depend on a parameter such as the image resolution beforedownsampling and/or the frame rate of the source.

For example, one can refer to Table 1 below, where the values markedwith an asterisk indicate the optimal choices of sets of Kaiser-BesselK-i coefficients according to the resolution and the frame rate (thedifference between situations A1 and A2 or between situations B1 and B2or B3 and B4) being related to the type of scene represented in theimage (face, landscape, or other).

These optimal choices minimize the percentage (negative) presented inTable 1, this percentage quantifying the optimization of the in finecoding efficiency according to the “Bjontegaard” metric.

TABLE 1 Resolution Initial K-1 K-1.25 K-1.5 K-1.75 K-2 K-2.25 K-2.5 UHD1−26.9% −25.4% −26.2% −26.8% −27.3%  −27.6%  −27.7%* −27.7%* (3840 ×2160, 30 fps) A1 −24.9% −24.6% −25.1% −25.6% −25.9%  −26.0%  −26.1%*−25.9%  (2560 × 1600, 30 fps) A2 −27.4% −24.8% −25.7% −26.4% −27.0% −27.4%  −27.6%* −27.6%* (2560 × 1600, 30 fps) B1 −29.6% −29.0% −29.3%−29.5% −29.6%* −29.6%* −29.6%* −29.5%  (1920 × 1080, 24 fps) B2 −21.9%−23.2% −23.6% −23.8% −23.9%* −23.8%  −23.7%  −23.5%  (1920 × 1080, 24fps) B3 −19.5% −19.8% −20.1% −20.3% −20.4%  −20.5%* −20.4%  −20.2% (1920 × 1080, 50 fps) B4 −16.3% −15.8% −16.0% −16.2% −16.4%* −16.4%*−16.4%* −16.3%  (1920 × 1080, 50 fps) B5 −12.3% −12.9% −13.2% −13.5%−13.6%  −13.7%* −13.7%* −13.5%  (1920 × 1080, 60 fps) Average −21.7%−21.4% −21.9% −22.2% −22.4%  −22.5%* −22.4%  −22.4% 

For example, for a resolution of 1920×1080 pixels, and with a frequencyof 24 frames per second (B1 or B2 above), one can choose as default theKaiser-Bessel K-1.75 configuration for any type of scene in the image.On the other hand, for the same resolution of 1920×1080 pixels but witha higher frequency (50 or 60 frames per second: B3 to B5), one canchoose as default the K-2 configuration.

For a resolution greater than 1920×1080 pixels (for example A1, A2 orUHD), a K-2.25 or K2.5 configuration can instead be chosen.

Reference is now made to FIG. 3 for the following description ofselecting one or more filters from a set of types of finite impulseresponse filters, to be applied to the filtering steps S112, S122presented above.

The initial image data stream S20 is pre-analyzed in step S21 totypically determine:

-   -   an initial frame rate,    -   the resolution before downsampling,    -   possibly a type of scene in the image, in particular with        information quantifying the importance of one or more areas in        the image, quantitatively determining the importance of an image        area for example according to the texture complexity of this        area.

Information about the coding rate of the images may also be useful.Indeed, the higher the rate the more information from the source imagemust be retained after filtering S112, S122.

This information from the pre-analysis S21 can then be used forselecting the filter (or combination of filters) in general step S30,according to estimations carried out in general step S22 and based onthe aforementioned criteria: frequency, resolution, score of the areasof interest in a current image, coding rate.

One of the selection criteria may be a resolution/frame rate in acontext of using a Kaiser-Bessel filter. In step S23, information on theresolution before downsampling is obtained and it is thus possible toselect in step S26 a configuration between K-1.75 and K-2. Then, in theabove-mentioned step S23, it is possible to obtain data on the framerate (for example 24 fps) and then definitively choose K-1.75 in stepS27. Otherwise (for example with a 50 fps frame rate), K-2 is insteadchosen in step S27.

Additionally or alternatively, one can choose a different configurationdynamically, according to the scene and its complexity. Alsoalternatively, one can choose yet another different type of filter (forexample bicubic or bilinear or other) according to the complexity of theimage and typically according to a score of the areas of interest in theimage in step S28. These areas of interest can be identified in theimage as described below (with a score based on the sharpness and theforeground or background position of the area), or predefined accordingto given location blocks in the image.

For example, in the pre-analysis step S21, it is possible to identifyareas of the image having a contrast or texture complexity above orbelow a threshold in step S24 (for example a face in focus in theforeground, or conversely a uniform sky in the background) and assign acomplexity score to each of these areas in step S28.

More particularly, it is possible to measure an interest of an areaprior to encoding and filtering, according to two processing steps:

-   -   first establishing a depth map,    -   and, in each of the identified planes, it is then possible to        detect and quantify blur.

Scores can then be given to each region of interest based on theircategory, for example:

-   -   0: Region sharp and in the foreground,    -   1: Region somewhat distant and sharp,    -   2: Region in the background and sharp,    -   3: Region in the foreground and fuzzy,    -   4: Region somewhat distant and fuzzy,    -   5: Region in the background and fuzzy.

Different types of filters can be used for each of these scores. Thesomewhat distant regions are all regions between the foreground and thebackground. Regions with a score less than or equal to 1 can beprocessed by a bicubic filter, those with a score comprised between 2and 3 can be processed by a bilinear filter, and finally those with ascore greater than or equal to 4 by an interpolation filter. For regionswith low scores (and therefore of strong interest), the codingefficiency is then improved in comparison to processing which only usesone downsampling filter on the reference layers.

Thus, in such an exemplary embodiment, it is then possible to select instep S30 a filter to be applied to each image area according to itsscore determined in step S28. For example, a region with a score lessthan or equal to 1 can be processed with a bicubic filter in step S33. Aregion with a score comprised between 2 and 3 can be filtered with abilinear filter in step S32. A region with a score greater than or equalto 4 can be processed with an interpolation filter in step S34.

Additionally or alternatively, it is possible to apply yet anothercriterion for the choice of filter type (in areas of complementaryimages for example) as a function of the coding rate of the referencelayers (L0 or L1). In step S29, the current value of the coding rate canbe compared to a predefined threshold in step S25. For example, when thecoding rate exceeds a threshold of 2 Mbits/sec in step S29, a bicubicfilter S33 can be used. Conversely, when the coding rate is less than orequal to 2 Mbits/sec S29, a bilinear filter can be selected in step S32.

According to another example, it may be useful, when the coding ratevaries, to set the predefined threshold according to a percentage of thecoding rate assigned either to the layer on which the filter is appliedor to all the layers.

Of course, we can combine the different types of filters that areapplied from one image to another and we can choose a different filter:

-   -   from one image to another in a succession of images, or    -   from one image to be encoded for a layer Li to another image to        be encoded for another layer Lj,    -   or within a same image, from one region of the image to another        region of the image.

More generally, the present disclosure is not limited to the embodimentsdescribed above as examples; it extends to other variants.

Thus, for example, it is possible to use filters other than finiteimpulse response type filters, the main criterion being that they are ofthe low-pass type in order to “smooth the textures” and thus effectivelyfilter the aliasing noise.

The invention claimed is:
 1. A method for coding image data, scalable inimage resolution, comprising: performing, based on a source signal ofmaximal image resolution, one or more successive downsamplings to aminimal image resolution, in order to encode a base layer correspondingto said minimal resolution, wherein the base layer is usable as areference for one or more enhancement layers to be used for decoding ata terminal having a screen of higher image resolution than the minimalimage resolution, constructing the base layer and the one or moreenhancement layers based on the one or more successive downsamplings,preparing an encoded bitstream to be transmitted, wherein the encodedbitstream comprises data of the base layer and data of the one or moreenhancement layers, wherein at least one of the one or more successivedownsamplings comprises: performing an image pre-analysis to determineat least an importance criterion to be given to one or more regions ofinterest in image data of the source signal, the importance criterionbeing a texture complexity score of a region of interest measured by: adetermination of a depth map in the current image, in order to identifya plurality of superimposed planes, and in each of the identifiedplanes, a detection and a quantification of blur, assigning of a scoreto a region according to a depth of a plane in which this region islocated, and a sharpness of an image in this plane, selecting at leastone of a plurality of predetermined types of adaptive filters based atleast in part upon the determined importance criterion, applying anadaptive low-pass filter of the selected type to the image data of thesource signal to reduce a spectral folding effect of the at least one ofthe one or more successive downsamplings.
 2. The method according toclaim 1, wherein the adaptive filter is of a finite impulse responsetype.
 3. The method according to claim 2, wherein the finite impulseresponse filter is of a type chosen among at least: a Kaiser-Besselwindow filter, a bicubic filter, a bilinear filter, an interpolationfilter.
 4. The method according to claim 1, wherein the type of adaptivefilter is selected for an image or an image region according to at leastone criterion among: an image resolution before downsampling, a framerate of the source signal, an image coding rate.
 5. The method accordingto claim 1, wherein a bicubic filter is selected for sharp foregroundregions, while a bilinear filter is used for sharp background regionsand/or fuzzy foreground regions, and otherwise an interpolation filteris used.
 6. The method according to claim 1, wherein a range ofconfigurations defining coefficients of a Kaiser-Bessel window filter ischosen based on a criterion among an image resolution beforedownsampling and a frame rate of the source signal.
 7. The methodaccording to claim 6, wherein the coefficients of the Kaiser-Besselwindow filter are chosen according to a configuration selected withinsaid range, based on other criterion among the image resolution beforedownsampling and the frame rate of the source signal.
 8. The methodaccording to claim 1, wherein a bicubic filter is selected if a codingrate of the base layer is greater than a threshold, and otherwise abilinear filter is selected.
 9. The method according to claim 8, whereinthe threshold is 2 Mbits/sec.
 10. An image data coding device, scalablein image resolution, comprising a processing circuit for implementingthe method according to claim
 1. 11. A computer program comprisinginstructions stored on a non-transitory computer-readable medium forimplementing the method according to claim 1, when said instructions areexecuted by a processor of the processing circuit.
 12. A method forcoding image data, scalable in image resolution, comprising: performing,based on a source signal of maximal image resolution, one or moresuccessive downsamplings to a minimal image resolution, in order toencode a base layer corresponding to said minimal resolution, whereinthe base layer is usable as a reference for one or more enhancementlayers to be used for decoding at a terminal having a screen of higherimage resolution than the minimal image resolution, constructing thebase layer and the one or more enhancement layers based on the one ormore successive downsamplings, preparing an encoded bitstream to betransmitted, wherein the encoded bitstream comprises data of the baselayer and data of the one or more enhancement layers, wherein at leastone of the one or more successive downsamplings comprises: performing animage pre-analysis to determine at least an importance criterion to begiven to one or more regions of interest in image data of the sourcesignal, the image pre-analysis identifying areas in the image datahaving a contrast or texture complexity above or below a predeterminedthreshold, the importance criterion being a texture complexity score ofa region of interest measured by: a determination of a depth map in thecurrent image, in order to identify a plurality of superimposed planes,and in each of the identified planes, a detection and a quantificationof blur, assigning of a score to a region according to a depth of aplane in which this region is located, and a sharpness of an image inthis plane, selecting at least one of a plurality of predetermined typesof adaptive filters based at least in part upon the determinedimportance criterion, applying an adaptive low-pass filter of theselected type to the image data of the source signal to reduce aspectral folding effect of the at least one of the one or moresuccessive downsamplings.